In today’s digital era, accessibility to technology across languages is essential to bridge communication gaps and foster inclusivity. Translation bridges communications between cultures, just as a Multilingual Semantic Object Identification Using Computer Vision and NLP does for visual recognition; instead of keeping users to one language, this system identifies real-world objects and shares results in multiple languages, making technology feel more accessible and user-friendly for diverse communities. The project will further demonstrate how perfectly vision and language can go hand in hand by combining deep learning-based object detection with natural language processing. This approach uses models pre-trained on recognizing objects, combined with translation tools and speech output, ensuring accuracy and real-time performance. Ultimately, the outcome of this system is to provide a helpful tool for its users that offers beneficial support for learning, accessibility, and digital inclusion. Consequently, this grounds the basis for more inclusive AI technologies in a multilingual world.